Sea Ice Age and Drift (SAGE)

Summary

For decades, scientists have monitored the location and thickness of sea ice, however sea ice age and drift reveal how long it endures through the seasons and how it travels across the polar oceans. Both quantities form the Sea Ice Essential Climate Variable specified by the Global Observing Climate System. This ESA Climate Change Initiative project will utilise decades of satellite data, advanced tracking techniques, and machine learning to create the first global climate data records of sea ice age and drift, supporting climate research from the Arctic to Antarctica.

Background

Sea ice is one of the most sensitive indicators of climate change. While satellite observations have enabled monitoring of sea-ice concentration, extent, and thickness for decades, the age of sea ice provides more than a snapshot of the ice cover: it tells us how long the ice has survived through melting seasons and export processes.

Sea-ice age provides valuable insights into the stability, vulnerability, and long-term memory of the polar ice cover. Older, multiyear ice is typically thicker, more stable, and more resistant to melting, while younger, first-year ice is thinner and more susceptible to climate variability. Over the past few decades, the Arctic has seen a marked loss of multiyear ice. In contrast, Antarctic sea ice is less well monitored, and its changes remain poorly understood.

In Lagrangian tracking, individual ice parcels are followed over time by analysing satellite-derived sea-ice motion vectors. If an ice parcel survives from one year to the next, its age is incremented. This method uses drift and concentration data, often derived from passive microwave sensors, to trace where ice moves and how long it persists. It captures the dynamic evolution of the ice pack and provides a physically meaningful estimate of age but is sensitive to errors in inputs, and limited by data gaps.

The second approach is direct classification, which interprets satellite signals, such as brightness temperature or backscatter, to distinguish broader categories of ice types, such as first-year ice or multiyear ice, based on distinct physical properties. This is done using thresholds or statistical classifiers applied to passive microwave or scatterometer data. While faster and more direct, classification methods are challenged by ambiguous conditions, such as melt onset, refreezing, or thin snow-covered ice that can mask signal differences.

Aims and Objectives

This project will produce the first consistent, long-term Climate Data Records (CDRs) for sea-ice age and sea-ice drift. These will be global in coverage and span the satellite era from the late 1970s to the present. To achieve this, the project will:

• Develop a hybrid sea-ice-age algorithm that combines Lagrangian tracking (following the ice) with ice classification from direct interpretation of satellite data.
• Back-extend the global sea-ice drift climate data record before 1991.
• Use multi-sensor satellite data from passive microwave radiometry and scatterometer, including ESA’s heritage missions.
• Explore machine learning techniques to improve classification under challenging conditions like melt and refreeze transitions and regions of vast areas of pancake ice.
• Validate products using in situ data, buoys, SAR, and other EO records.
• Provide uncertainty estimates and traceable documentation.
• Demonstrate usage in scientific case studies.

These data records will support a wide range of applications, from understanding Arctic multiyear ice loss to detecting shifts in Antarctic sea-ice regimes and benchmarking the performance of climate models (CMIP7/SIMIP).

Project plans

The project runs from 2025 to 2028 and is structured into five core work packages:

• WP100 – User Needs & Requirements: Define what the community needs from the sea-ice age and drift records.
• WP200 – Algorithm R&D: Develop the core methods for retrieving ice age and type from EO data.
• WP300 – Processing System: Build the open-source system that will produce and distribute the products.
• WP400 – Validation: Quantify product quality and uncertainties, and provide independent evaluation.
• WP500 – Scientific Exploitation: Use the products in climate research, including studies of MYI budgets, sea-ice trends, and model evaluation.

A mid-project user workshop will bring together the modeling and cryosphere communities to review results and guide refinement. Final products will be documented and released for public and scientific use via the CCI Open Data Portal.